{"id":6877,"date":"2023-11-01T18:16:36","date_gmt":"2023-11-01T12:46:36","guid":{"rendered":"https:\/\/www.isical.ac.in\/~pamu\/?post_type=event&p=6877"},"modified":"2023-11-24T19:07:09","modified_gmt":"2023-11-24T13:37:09","slug":"one-day-lecture-series","status":"publish","type":"event","link":"https:\/\/oldweb.isical.ac.in\/~pamu\/event\/one-day-lecture-series\/","title":{"rendered":"One-day lecture series"},"content":{"rendered":"

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \"\"\u00a0 \u00a0 \"\"<\/h4>\n

<\/h4>\n

Time: 11:00 am<\/strong><\/h4>\n

Tipping in an ecological system under external forcing<\/strong><\/h4>\n
Syamal K. Dana<\/strong>
\nJadavpur University, India<\/h6>\n

Tipping or critical transitions in climate, ocean circulation, Greenland ice cap and Indian monsoon, and ecological systems known as regime shift, occurs due to faster changes in system parameters under the influence of external conditions. In recent time, major attention has been attracting researchers from various disciplines on studies of tipping using model systems that represents various natural phenomena. It mainly focused near the saddle-node bifurcation points that mostly express the dynamical features of the above systems. We use an ecological model to explore tipping against the time-varying carrying capacity of the system. If the carrying capacity is varied at a linear rate, the system does not show sharp transitions as expected immediately at the bifurcation points but tips to the alternate states after an elapse of time. Additionally, we consider any impacts of environmental shocks which is modeled by a triangular shape impulse. Delayed tipping occurs in such a situation of external shock but shows a dependence on the falling and rising rates of the impulse. The active time window of the external impulse on the carrying capacity called as exceedance time plays a decisive role on the occurrence of tipping. Furthermore, we apply a second impulse, in case the first impulse fails to induce any tipping. The role of the rate parameters and strength of the impulses, and most importantly, the time interval of the impulses is considered in detail to delineate the tipping zones in parameter space.<\/p>\n

Time: 11:30 am<\/strong><\/h4>\n

Mathematical modelling of spiking neural\u00a0networks accompanied by astrocytes<\/strong><\/h4>\n
Susanna Yu. Gordleeva<\/strong>
\nLobachevsky State University of Nizhny Novgorod, Russia<\/h6>\n

Spiking neural networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a \u201cliving computer\u201d based on SNNs. We show how\u00a0SNN implements associative learning by exploiting the spatial properties of\u00a0spike-timing-dependent plasticity (STDP).\u00a0Accumulated data over the past decade shows that long-neglected glial cells, especially astrocytes, are intricately involved in the activity of neural networks.\u00a0It has been shown that astrocytes exhibit a high degree of heterogeneity concerning gene expression profiles, morphology, synaptic inputs responsiveness and in their subsequent Ca2+-activity responses. This huge heterogeneity is observed on different levels, e.g. in different brain regions, cortical layers, and different neural circuits. In addition, it has become clear that diffuse extracellular signaling is also very critical for brain functions. Such signals directly affect information transfer and storage in the neuronal networks. Astroglial cells have been highlighted as critical players in the activity modulation of neural networks and the generation of physiological signals by fastening local fluctuations and nonlinear diffusion of intracellular Ca2+-wave.<\/p>\n

Time: 12:30 pm<\/strong><\/h4>\n

Synchronization in higher-order networks<\/strong><\/h4>\n
Md Sayeed Anwar<\/strong>
\nIndian Statistical Institute, India<\/h6>\n

The stability analysis of synchronization in time-varying higher-order networked structures (simplicial complexes) is a challenging problem due to the presence of time-varying group interactions. In this context, most of the previous studies have been done either on temporal pairwise networks or on static simplicial complexes. Here, we discuss a general framework to study the synchronization phenomenon in temporal simplicial complexes. We show that the synchronous state exists as an invariant solution and obtain the necessary condition for it to emerge as a stable state in the fast-switching regime. We prove that the time-averaged simplicial complex plays the role of synchronization indicator whenever the switching among simplicial topologies is adequately fast. We attempt to transform the stability problem into a master stability function form. Unfortunately, for the general circumstances, the dimension reduction of the master stability equation is cumbersome due to the presence of group interactions. However, we overcome this difficulty in two interesting situations based on either the functional forms of the coupling schemes or the connectivity structure of the simplicial complex, and we demonstrate that the necessary condition mimics the form of a master stability function in these cases. We verify our analytical findings by applying them on synthetic and real-world networked systems. We find that the presence of temporality along with the multiway interactions improves the synchronization phenomena as compared to the static higher-ordered or temporal pairwise system. In addition, our results also reveal that with sufficient higher-order coupling and adequately fast rewiring, the temporal simplicial complex achieves synchrony even in a very low connectivity regime.<\/p>\n

Time: 3:00 pm<\/strong><\/h4>\n

Diagnostics and study of mental disorders based on the analysis of fMRI-derived functional brain networks<\/strong><\/h4>\n
Semen A. Kurkin<\/strong>
\nImmanuel Kant Baltic Federal University, Russia<\/h6>\n

In this talk, I will present the results of our studies of the fMRI-derived functional brain networks of patients with major depressive disorder (MDD), which aimed to identify characteristic abnormalities in their functional networks and to develop effective classifiers for diagnosing MDD. I will focus on the following points: network-level statistical analysis, analysis of the standard network measures and topology features, development of simple ML classifiers and their interpretability, application of graph neural networks, and the investigation of high-order interactions in the functional brain networks.<\/p>\n

Time: 3:30 pm<\/strong><\/h4>\n
Reservoir computing approach for analysis and prediction of complex network dynamics<\/strong><\/h5>\n
Andrey V. Andreev<\/strong>
\nImmanuel Kant Baltic Federal University, Russia<\/h6>\n

Prediction of a system\u2019s behavior is an essential task encountering the complex networks theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predicts the behavior of model systems whose states consist of multidimensional time series. In real life, we often have limited information about the behavior of complex networks. The brightest example is the brain neural network described by the electroencephalogram. Prediction of the behavior of these systems is a more challenging task but provides a potential for real-life application. In the current work, we train reservoir computer to predict the macroscopic signal produced by the adaptive network of phase oscillators. The Lyapunov analysis revealed the chaotic nature of the signal and reservoir computer failed to forecast it. Augmenting the feature space using Takkens\u2019 theorem improved the quality of forecasting. RC achieved the best prediction score when the number of signals coincided with the embedding dimension estimated via the nearest false neighbors method. Another application of RC is to restore the missing signals of the network\u2019s nodes by the neighbors\u2019 dynamics. We show that neural network solves the task better than the approximation methods, and even one neighbor\u2019s dynamics is enough to restore the missing signal with a high accuracy.<\/p>\n

Time: 4:30 pm<\/strong><\/h4>\n
Dynamics of swarmalators with higher-order interactions<\/strong><\/h5>\n
Gourab Kumar Sar<\/strong>
\nIndian Statistical Institute, India<\/h6>\n

Higher-order interactions shape collective dynamics, but how they affect transitions between different states in swarmalator systems is yet to be determined. To that effect, we here study an analytically tractable swarmalator model that incorporates both pairwise and higher-order interactions, resulting in four distinct collective states: async, phase wave, mixed, and sync states. We show that even a minute fraction of higher-order interactions induces abrupt transitions from the async state to the phase wave and the sync state. We also show that higher-order interactions facilitate an abrupt transition from the phase wave to the sync state by bypassing the intermediate mixed state. Moreover, elevated levels of higher- order interactions can sustain the presence of phase wave and sync state, even when pairwise interactions lean towards repulsion. The insights gained from these findings unveil self-organizing processes that hold the potential to explain sudden transitions between various collective states in numerous real-world systems.<\/p>\n","protected":false},"excerpt":{"rendered":"

The Physics and Applied Mathematics Unit is organising one day lecture series on the topic of (1) Tipping in an ecological system under external forcing by Syamal K. Dana, (2) Mathematical modelling of spiking neural\u00a0networks accompanied by astrocytes by Susanna Yu. Gordleeva, (3) Synchronization in higher-order networks by Md Sayeed Anwar, (4) Diagnostics and study of mental disorders based on the analysis of fMRI-derived functional brain networks by Semen A. Kurkin, (5) Reservoir computing approach for analysis and prediction of complex network dynamics by Andrey V. Andreev, and (6) Dynamics of swarmalators with higher-order interactions by Gourab Kumar Sar<\/p>\n","protected":false},"featured_media":6880,"template":"","categories":[107],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/event\/6877"}],"collection":[{"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/event"}],"about":[{"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/types\/event"}],"version-history":[{"count":1,"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/event\/6877\/revisions"}],"predecessor-version":[{"id":7147,"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/event\/6877\/revisions\/7147"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/media\/6880"}],"wp:attachment":[{"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/media?parent=6877"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/categories?post=6877"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/oldweb.isical.ac.in\/~pamu\/wp-json\/wp\/v2\/tags?post=6877"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}